• Journal of the Korean Orthopaedic Association
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• v.56 no.5
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• pp.398-403
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• 2021

Purpose: A biopsy is needed to diagnose soft tissue tumors. However, it is extremely difficult to pinpoint the site of a tumor due to the heterogeneity of sarcomas. Thus, even when an open biopsy is conducted, it is difficult to diagnose a soft tissue tumor. In such cases, an ultrasound (US)-guided biopsy is used to improve the diagnostic accuracy. This study evaluated the accuracy of US-guided biopsy for a diagnosis of soft tissue tumors found initially in a magnetic resonance (MR) perfusion and assessed the availability of positron emission tomography-computed tomography (PET-CT) for a diagnosis of soft tissue tumors. Materials and Methods: From January 2014 to December 2018, the US-guided biopsy was performed on 152 patients with a suspected soft tissue tumor found in an MR perfusion and 86 cases were definitively diagnosed with a soft tissue tumor. The accuracy of the US-guided biopsy was assessed retrospectively. Among the 86 cases, only MR perfusion was used before the biopsy in 50 cases, while both MR perfusion and PET-CT was conducted on 36 cases. The accuracy was analyzed to determine if the PET-CT could improve the precision of a biopsy. Results: From 86 cases, 34 out of 50 cases, in which only MR perfusion had been conducted, matched the result of the definitive diagnosis and the US-guided biopsy. 32 out of 36 cases, in which both PET-CT and MR perfusion were conducted, matched the definitive diagnosis and the US-guided biopsy. These results show significant differences in the accuracy of US-guided biopsy. In the case of soft tissue sarcomas, 6 out of 12 cases, in which only MR perfusion had been conducted, matched the result of the definitive diagnosis and the US-guided biopsy. 17 out of 18 cases, in which both PET-CT and MR perfusion were conducted, matched the definitive diagnosis. Moreover US-guided biopsy also showed significant differences in the accuracy of US-guided biopsy. Conclusion: In diagnosing soft tissue tumors, a US-guided biopsy is a well-known tool for its high accuracy. However, the heterogeneity of sarcoma makes it difficult to locate the exact site for a biopsy using only MR perfusion. Thus, the use of PET-CT will meaningfully improve the accuracy of a diagnosis by precisely targeting the site for the US-guided biopsy.

• Journal of the Korea Computer Graphics Society
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• v.2 no.1
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• pp.77-85
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• 1996

Using a series of medical tomograms, we can reconstruct internal organs or other objects of interest and generate 3-D images. It is generally accepted that the axial resolution determined by two sequential image slices is lower than the planar resolution in one image slices. Therefore, various methods of interpolation were developed for an accurate display of reconstructed images. In this paper, a new algorithm for 3-D reconstruction of the medical images such as MRI and X-ray CT is suggested. The algorithm is shape-based and utilizes parts of the gray-level information. We extend the conventional shape-based interpolation of the binary images to the gray-scale images using the shortest distance map. Using this new algorithm, We could reduce the execution time for interpolation while keeping similar high quality of the reconstructed images with reduced execution time and is applicable to the various medical tomograms.

• Journal of Biomedical Engineering Research
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• v.20 no.6
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• pp.523-529
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• 1999

방출컴퓨터단층영상술을 위한 영상재구성법에 있어서 기대값 최대화(EM)를 사용한 maximum likeihood 방법이 기존의 filtered backprojection 방법에 비해 현저한 장점을 지니고 있다는 점에서 지속적으로 그 가치가 인정되어 왔다. 그러나, 이러한 방법은 projection 및 backprojection 의 반복계산을 요하므로 영상재구성을 위한 총 계산시간이 projector 및 backprojector 의 성능에 크게 좌우된다. 본 논문에서는 EM에 근거한 영상재구성 알고리즘의 계산량을 감소시키는 방법에 관하여 논한다. 특히, projection 및 backprojection 계산을 위한 행렬의 원소중 중요한 양들을 구하는 방법과 이들을 미리 계산하여 적절한 양의 메모리에 저장하는 방법에 관하여 고찰한다. 실험에서 제안된 방법을 사용할 경우 EM 알고리즘의 계산시간을 92%까지 현저히 감소시킬 수있음을 보였다.

• Journal of the Korea Computer Industry Society
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• v.3 no.7
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• pp.901-914
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• 2002

In this paper, We propose an efficient automatic caption detection and location method, caption recognition using FE-MCBP(Feature Extraction based Multichained BackPropagation) neural network for content based retrieval of video. Frames are selected at fixed time interval from video and key frames are selected by gray scale histogram method. for each key frames, segmentation is performed and caption lines are detected using line scan method. lastly each characters are separated. This research improves speed and efficiency by color segmentation using local maximum analysis method before line scanning. Caption detection is a first stage of multimedia database organization and detected captions are used as input of text recognition system. Recognized captions can be searched by content based retrieval method.

• Journal of Dental Rehabilitation and Applied Science
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• v.31 no.1
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• pp.67-74
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• 2015

Computer-guided surgery utilizing computed tomography is advantageous in placing implants precisely and conveniently. The purpose of the cases was to report a newly developed CT-based guide fabrication program and subtractive manufacturing using resin block. The guided surgery with the program and subtractive manufacturing allows not only precise translation of the treatment plan, but also offers additional significant benefits.

• Journal of the Korean Society of Radiology
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• v.84 no.1
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• pp.110-126
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• 2023

Because of their small size, small bowel malignancies are often easily missed when radiologists read CT images, which can lead to errors in the normal small bowel due to their similar enhancement to the small bowel or non-specific enhancement. In addition, small bowel malignancies are sometimes mistaken for masses of other origins, such as the uterus, rectum, or omentum. In this article, we describe various small-bowel malignancies, their mimicking lesions and the typical and atypical features on CT imaging along with their clinical manifestations.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.63-65
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• 2023

본 논문에서는 FDG-PET 영상을 사용하는 딥러닝 네트워크를 이용하여 직장암 환자의 치료 후 완치를 예측하는 연구를 수행하였다. 직장암은 흔한 악성 종양 중 하나이지만 병리학적으로 완전하게 치료되는 가능성이 매우 낮아, 치료 후의 반응을 예측하고 적절한 치료 방법을 선택하는 것이 중요하다. 따라서 본 연구에서는 FDG-PET 영상에 합성곱 신경망(CNN)모델을 활용하여 딥러닝 네트워크를 구축하고 직장암 환자의 치료반응을 예측하는 연구를 진행하였다. 116명의 직장암 환자의 FDG-PET 영상을 획득하였다. 대상군은 2cm 이상의 종양 크기를 가지는 환자를 대상으로 하였으며 치료 후 완치된 환자는 21명이었다. FDG-PET 영상은 전신 영역과 종양 영역으로 나누어 평가하였다. 딥러닝 네트워크는 2차원 및 3차원 영상입력에 대한 CNN 모델로 구성되었다. 학습된 CNN 모델을 사용하여 직장암의 치료 후 완치를 예측하는 성능을 평가하였다. 학습 결과에서 평균 정확도와 정밀도는 각각 0.854와 0.905로 나타났으며, 모든 CNN 모델과 영상 영역에 따른 성능을 보였다. 테스트 결과에서는 3차원 CNN 모델과 종양 영역만을 이용한 네트워크에서 정확도가 높게 평가됨을 확인하였다. 본 연구에서는 CNN 모델의 입력 영상에 따른 차이와 영상 영역에 따른 딥러닝 네트워크의 성능을 평가하였으며 딥러닝 네트워크 모델을 통해 직장암 치료반응을 예측하고 적절한 치료 방향 결정에 도움이 될 것으로 기대한다.

• Journal of the Korean Society of Radiology
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• v.10 no.2
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• pp.81-87
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• 2016

In this study we proposed a texture feature analysis algorithm that distinguishes between a normal image and a diseased image using CT images of some fatty liver patients, and generates both Eigen images and test images which can be applied to the proposed computer aided diagnosis system in order to perform a quantitative analysis for 6 parameters. And through the analysis, we derived and evaluated the recognition rate of CT images of fatty liver. As the results of examining over 30 example CT images of fatty liver, the recognition rates representing a specific texture feature-value are as follows: some appeared to be as high as 100% including Average Gray Level, Entropy 96.67%, Skewness 93.33%, and Smoothness while others showed a little low disease recognition rate: 83.33% for Uniformity 86.67% and for Average Contrast 80%. Consequently, based on this research result, if a software that enables a computer aided diagnosis system for medical images is developed, it will lead to the availability for the automatic detection of a diseased spot in CT images of fatty liver and quantitative analysis. And they can be used as computer aided diagnosis data, resulting in the increased accuracy and the shortened time in the stage of final reading.

• Journal of the Korean Society of Radiology
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• v.83 no.4
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• pp.924-930
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• 2022

Most spinal meningiomas have an intradural or partly extradural location. The meningothelial origin is the most common pathologic type of spinal meningioma. Pure extradural spinal meningiomas are not common, and lymphoplasmacyte-rich meningioma (LPRM) is very rare. We report a case of isolated extradural spinal meningioma in the thoracic spine that was pathologically confirmed as LPRM.

• Journal of the Korean Society of Radiology
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• v.1 no.2
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• pp.23-30
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• 2007

This paper presents a method of generating 3-dimensional images by preprocessing 2-dimensional abdominal images obtained using CT (computed tomography) and MRI (magnetic resonance imaging) through segmentation, threshold technique, etc. and apply the method to virtual endoscopy. Three-dimensional images were visualized using indirect volume rendering, which can render at high speed using a general-purpose graphic accelerator used in personal computers. The algorithm used in the rendering is Marching Cubes, which has only a small volume of calculation. In addition, we suggested a method of producing 3-dimensional images in VRML (virtual reality modeling language) running on the Web browser without a workstation or an exclusive program. The number of nodes, the number of triangles and the size of a 3-dimensional image file from CT were 85,367, 174,150 and 10,124, respectively, and those from MRI were 34,029, 67,824 and 3,804, respectively.